%0 Journal Article
%T Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks
%A Philipp Rumschinski
%A Steffen Borchers
%A Sandro Bosio
%A Robert Weismantel
%A Rolf Findeisen
%J BMC Systems Biology
%D 2010
%I BioMed Central
%R 10.1186/1752-0509-4-69
%X In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort.The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.Mathematical modeling has become an important tool for analysis and prediction of metabolic and signal transduction processes [1,2]. Given a biological system and some experimental evidence, deriving a model hypothesis that captures the essential behavior of the system under study is a nontrivial task. Limited prior knowledge on the involved reaction mechanisms and signaling pathways may lead to competing structural hypotheses, whose parameters might be completely or largely unknown. Moreover, the model dynamics are typically strongly influenced by the model parameters [3,4]. An accurate parameter estimation is thus a crucial step to conclusively discriminate between structural alternatives, allowing to discard models for which it can be proved that no parametrization is consistent with the experimental evidence.Model invalidation and parameter estimation are considerably more challenging in biology than in other experimental and engineering sciences, requiring specifically tailored methods. Experiments are usually
%U http://www.biomedcentral.com/1752-0509/4/69